



'''simple docstring'''






import inspect

import jax
import jax.lax as lax
import jax.numpy as jnp

from ..utils import add_start_docstrings
from ..utils.logging import get_logger


lowerCAmelCase:   Optional[int]					=		get_logger(__name__)


lowerCAmelCase:   Union[str, Any]					=		r'\n    Args:\n        input_ids (`jnp.ndarray` of shape `(batch_size, sequence_length)`):\n            Indices of input sequence tokens in the vocabulary.\n\n            Indices can be obtained using [`PreTrainedTokenizer`]. See [`PreTrainedTokenizer.encode`] and\n            [`PreTrainedTokenizer.__call__`] for details.\n\n            [What are input IDs?](../glossary#input-ids)\n        scores (`jnp.ndarray` of shape `(batch_size, config.vocab_size)`):\n            Prediction scores of a language modeling head. These can be logits for each vocabulary when not using beam\n            search or log softmax for each vocabulary token when using beam search\n        kwargs (`Dict[str, Any]`, *optional*):\n            Additional logits processor specific kwargs.\n\n    Return:\n        `jnp.ndarray` of shape `(batch_size, config.vocab_size)`: The processed prediction scores.\n\n'

class 				a__:







    @add_start_docstrings(__snake_case      )
    def __call__( self					:    Optional[Any]		,     __snake_case					:    jnp.ndarray		,     __snake_case					:    jnp.ndarray      ):
      raise NotImplementedError(
          F"""{self.__class__} is an abstract class. Only classes inheriting this class can be called."""      )

class 				a__:







    @add_start_docstrings(__snake_case      )
    def __call__( self					:    Tuple		,     __snake_case					:    jnp.ndarray		,     __snake_case					:    jnp.ndarray      ):
      raise NotImplementedError(
          F"""{self.__class__} is an abstract class. Only classes inheriting this class can be called."""      )

class 				a__(   lowerCamelCase__      ):







    @add_start_docstrings(__snake_case      )
    def __call__( self					:    Optional[int]		,     __snake_case					:    jnp.ndarray		,     __snake_case					:    jnp.ndarray		,     __snake_case					:    int		,     **__snake_case					:    str      ):
      for processor in self:
        a		:   Optional[Any]	  =	inspect.signature(processor.__call__      ).parameters
        if len(__snake_case      ) > 3:
          if not all(arg in kwargs for arg in list(function_args.keys()      )[2:]      ):
            raise ValueError(
                F"""Make sure that all the required parameters: {list(function_args.keys()      )} for """
                F"""{processor.__class__} are passed to the logits processor."""      )
          a		:   str	  =	processor(__snake_case		,     __snake_case		,     __snake_case		,     **__snake_case      )
        else:
          a		:   Union[str, Any]	  =	processor(__snake_case		,     __snake_case		,     __snake_case      )
      return scores

class 				a__(   lowerCamelCase__      ):







    def __init__( self					:    Any		,     __snake_case					:    float      ):
      if not isinstance(__snake_case		,     __snake_case      ) or not (temperature > 0):
        raise ValueError(F"""`temperature` has to be a strictly positive float, but is {temperature}"""      )

      a		:   int	  =	temperature







    def __call__( self					:    Optional[int]		,     __snake_case					:    jnp.ndarray		,     __snake_case					:    jnp.ndarray		,     __snake_case					:    int      ):
      a		:   List[str]	  =	scores / self.temperature
      return scores

class 				a__(   lowerCamelCase__      ):







    def __init__( self					:    Tuple		,     __snake_case					:    float		,     __snake_case					:    float = -float('Inf'      )		,     __snake_case					:    int = 1      ):
      if not isinstance(__snake_case		,     __snake_case      ) or (top_p < 0 or top_p > 1.0):
        raise ValueError(F"""`top_p` has to be a float > 0 and < 1, but is {top_p}"""      )
      if not isinstance(__snake_case		,     __snake_case      ) or (min_tokens_to_keep < 1):
        raise ValueError(F"""`min_tokens_to_keep` has to be a positive integer, but is {min_tokens_to_keep}"""      )

      a		:   str	  =	top_p
      a		:   Any	  =	filter_value
      a		:   Tuple	  =	min_tokens_to_keep







    def __call__( self					:    str		,     __snake_case					:    jnp.ndarray		,     __snake_case					:    jnp.ndarray		,     __snake_case					:    int      ):
      a  ,      a		:   List[Any]	  =	lax.top_k(__snake_case		,     scores.shape[-1]      )

      a		:   Tuple	  =	jnp.full_like(__snake_case		,     self.filter_value      )
      a		:   Dict	  =	jax.nn.softmax(__snake_case		,     axis=-1      ).cumsum(axis=-1      )
      a		:   str	  =	cumulative_probs < self.top_p

      # include the token that is higher than top_p as well
      a		:   Union[str, Any]	  =	jnp.roll(__snake_case		,     1      )
      score_mask |= score_mask.at[:, 0].set(__snake_case      )

      # min tokens to keep
      a		:   Union[str, Any]	  =	score_mask.at[:, : self.min_tokens_to_keep].set(__snake_case      )

      a		:   Dict	  =	jnp.where(__snake_case		,     __snake_case		,     __snake_case      )
      a		:   Tuple	  =	jax.lax.sort_key_val(__snake_case		,     __snake_case      )[-1]

      return next_scores

class 				a__(   lowerCamelCase__      ):







    def __init__( self					:    Dict		,     __snake_case					:    int		,     __snake_case					:    float = -float('Inf'      )		,     __snake_case					:    int = 1      ):
      if not isinstance(__snake_case		,     __snake_case      ) or top_k <= 0:
        raise ValueError(F"""`top_k` has to be a strictly positive integer, but is {top_k}"""      )

      a		:   Union[str, Any]	  =	max(__snake_case		,     __snake_case      )
      a		:   List[Any]	  =	filter_value







    def __call__( self					:    Any		,     __snake_case					:    jnp.ndarray		,     __snake_case					:    jnp.ndarray		,     __snake_case					:    int      ):
      a  ,      a		:   Dict	  =	scores.shape
      a		:   Any	  =	jnp.full(batch_size * vocab_size		,     self.filter_value      )

      a		:   Tuple	  =	min(self.top_k		,     scores.shape[-1]      )  # Safety check
      a  ,      a		:   List[Any]	  =	lax.top_k(__snake_case		,     __snake_case      )
      a		:   int	  =	jnp.broadcast_to((jnp.arange(__snake_case      ) * vocab_size)[:, None]		,     (batch_size, topk)      ).flatten()
      a		:   Any	  =	topk_scores.flatten()
      a		:   str	  =	topk_indices.flatten() + shift

      a		:   Tuple	  =	next_scores_flat.at[topk_indices_flat].set(__snake_case      )
      a		:   Optional[int]	  =	next_scores_flat.reshape(__snake_case		,     __snake_case      )
      return next_scores

class 				a__(   lowerCamelCase__      ):







    def __init__( self					:    Any		,     __snake_case					:    int      ):
      a		:   Tuple	  =	bos_token_id







    def __call__( self					:    Dict		,     __snake_case					:    jnp.ndarray		,     __snake_case					:    jnp.ndarray		,     __snake_case					:    int      ):
      a		:   Optional[int]	  =	jnp.full(scores.shape		,     -float('inf'      )      )

      a		:   int	  =	1 - jnp.bool_(cur_len - 1      )

      a		:   Any	  =	jnp.where(__snake_case		,     new_scores.at[:, self.bos_token_id].set(0      )		,     __snake_case      )

      return scores

class 				a__(   lowerCamelCase__      ):







    def __init__( self					:    Union[str, Any]		,     __snake_case					:    int		,     __snake_case					:    int      ):
      a		:   Optional[int]	  =	max_length
      a		:   Optional[Any]	  =	eos_token_id







    def __call__( self					:    List[Any]		,     __snake_case					:    jnp.ndarray		,     __snake_case					:    jnp.ndarray		,     __snake_case					:    int      ):
      a		:   Dict	  =	jnp.full(scores.shape		,     -float('inf'      )      )

      a		:   Dict	  =	1 - jnp.bool_(cur_len - self.max_length + 1      )

      a		:   Tuple	  =	jnp.where(__snake_case		,     new_scores.at[:, self.eos_token_id].set(0      )		,     __snake_case      )

      return scores

class 				a__(   lowerCamelCase__      ):







    def __init__( self					:    int		,     __snake_case					:    int		,     __snake_case					:    int      ):
      if not isinstance(__snake_case		,     __snake_case      ) or min_length < 0:
        raise ValueError(F"""`min_length` has to be a positive integer, but is {min_length}"""      )

      if not isinstance(__snake_case		,     __snake_case      ) or eos_token_id < 0:
        raise ValueError(F"""`eos_token_id` has to be a positive integer, but is {eos_token_id}"""      )

      a		:   str	  =	min_length
      a		:   Optional[Any]	  =	eos_token_id







    def __call__( self					:    Optional[Any]		,     __snake_case					:    jnp.ndarray		,     __snake_case					:    jnp.ndarray		,     __snake_case					:    int      ):
      # create boolean flag to decide if min length penalty should be applied
      a		:   List[Any]	  =	1 - jnp.clip(cur_len - self.min_length		,     0		,     1      )

      a		:   Any	  =	jnp.where(__snake_case		,     scores.at[:, self.eos_token_id].set(-float('inf'      )      )		,     __snake_case      )

      return scores

class 				a__(   lowerCamelCase__      ):







    def __init__( self					:    Dict		,     __snake_case					:    Any		,     __snake_case					:    Optional[int]      ):
      a		:   Optional[Any]	  =	list(__snake_case      )
      a		:   Union[str, Any]	  =	begin_index







    def __call__( self					:    Any		,     __snake_case					:    Dict		,     __snake_case					:    int		,     __snake_case					:    int      ):
      a		:   Union[str, Any]	  =	1 - jnp.bool_(cur_len - self.begin_index      )

      a		:   int	  =	jnp.where(__snake_case		,     scores.at[:, self.begin_suppress_tokens].set(-float('inf'      )      )		,     __snake_case      )

      return scores

class 				a__(   lowerCamelCase__      ):







    def __init__( self					:    Dict		,     __snake_case					:    list      ):
      a		:   Any	  =	list(__snake_case      )







    def __call__( self					:    Tuple		,     __snake_case					:    jnp.ndarray		,     __snake_case					:    jnp.ndarray		,     __snake_case					:    int      ):
      a		:   List[Any]	  =	scores.at[..., self.suppress_tokens].set(-float('inf'      )      )

      return scores

class 				a__(   lowerCamelCase__      ):







    def __init__( self					:    List[Any]		,     __snake_case					:    Dict      ):
      a		:   Optional[int]	  =	dict(__snake_case      )
      # Converts the dictionary of format {index: token} containing the tokens to be forced to an array, where the
      # index of the array corresponds to the index of the token to be forced, for XLA compatibility.
      # Indexes without forced tokens will have a negative value.
      a		:   Any	  =	jnp.ones((max(force_token_map.keys()      ) + 1)		,     dtype=jnp.intaa      ) * -1
      for index, token in force_token_map.items():
        if token is not None:
          a		:   Union[str, Any]	  =	force_token_array.at[index].set(__snake_case      )
      a		:   List[str]	  =	jnp.intaa(__snake_case      )







    def __call__( self					:    int		,     __snake_case					:    jnp.ndarray		,     __snake_case					:    jnp.ndarray		,     __snake_case					:    int      ):
      def _force_token(__snake_case					:    Optional[Any]      ):
        a		:   Dict	  =	scores.shape[0]
        a		:   Tuple	  =	self.force_token_array[generation_idx]

        a		:   int	  =	jnp.ones_like(__snake_case		,     dtype=scores.dtype      ) * -float('inf'      )
        a		:   Union[str, Any]	  =	jnp.zeros((batch_size, 1)		,     dtype=scores.dtype      )
        a		:   Dict	  =	lax.dynamic_update_slice(__snake_case		,     __snake_case		,     (0, current_token)      )
        return new_scores

      a		:   int	  =	lax.cond(
          cur_len >= self.force_token_array.shape[0]		,     lambda: scores		,     lambda: lax.cond(
              self.force_token_array[cur_len] >= 0		,     lambda: _force_token(__snake_case      )		,     lambda: scores		,     )		,     )
      return scores

class 				a__(   lowerCamelCase__      ):







    def __init__( self					:    Any		,     __snake_case					:    int		,     __snake_case					:    Optional[Any]		,     __snake_case					:    Union[str, Any]      ):
      a		:   Union[str, Any]	  =	generate_config.eos_token_id
      a		:   Dict	  =	generate_config.no_timestamps_token_id
      a		:   List[str]	  =	generate_config.no_timestamps_token_id + 1

      a		:   Any	  =	decoder_input_length + 1

      if generate_config.is_multilingual:
        # room for language token and task token
        self.begin_index += 2
      if hasattr(__snake_case		,     'max_initial_timestamp_index'      ):
        a		:   List[str]	  =	generate_config.max_initial_timestamp_index
      else:
        a		:   int	  =	model_config.vocab_size
      if self.max_initial_timestamp_index is None:
        a		:   Any	  =	model_config.vocab_size







    def __call__( self					:    str		,     __snake_case					:    Optional[Any]		,     __snake_case					:    List[Any]		,     __snake_case					:    Union[str, Any]      ):
      # suppress <|notimestamps|> which is handled by without_timestamps
      a		:   Union[str, Any]	  =	scores.at[:, self.no_timestamps_token_id].set(-float('inf'      )      )

      def handle_pairs(__snake_case					:    str		,     __snake_case					:    Any      ):
        a		:   Tuple	  =	jnp.where((cur_len - self.begin_index) >= 1		,     __snake_case		,     __snake_case      )
        a		:   List[str]	  =	jnp.where(
            input_ids_k[cur_len - 1] >= self.timestamp_begin		,     True and last_was_timestamp		,     __snake_case		,     )

        a		:   str	  =	jnp.where((cur_len - self.begin_index) < 2		,     __snake_case		,     __snake_case      )
        a		:   Dict	  =	jnp.where(
            input_ids_k[cur_len - 2] >= self.timestamp_begin		,     __snake_case		,     __snake_case		,     )

        return jnp.where(
            __snake_case		,     jnp.where(
                penultimate_was_timestamp > 0		,     scores_k.at[self.timestamp_begin :].set(-float('inf'      )      )		,     scores_k.at[: self.eos_token_id].set(-float('inf'      )      )		,     )		,     __snake_case		,     )

      a		:   List[str]	  =	jax.vmap(__snake_case      )(__snake_case		,     __snake_case      )

      a		:   Any	  =	jnp.where(cur_len == self.begin_index		,     __snake_case		,     __snake_case      )
      a		:   List[Any]	  =	jnp.where(
          self.max_initial_timestamp_index is not None		,     True and apply_max_initial_timestamp		,     __snake_case		,     )

      a		:   Dict	  =	self.timestamp_begin + self.max_initial_timestamp_index

      a		:   Dict	  =	jnp.where(
          __snake_case		,     scores.at[:, last_allowed + 1 :].set(-float('inf'      )      )		,     __snake_case		,     )

      # if sum of probability over timestamps is above any other token, sample timestamp
      a		:   Union[str, Any]	  =	jax.nn.log_softmax(__snake_case		,     axis=-1      )

      def handle_cumulative_probs(__snake_case					:    Any		,     __snake_case					:    Union[str, Any]      ):
        a		:   Any	  =	jax.nn.logsumexp(logprobs_k[self.timestamp_begin :]		,     axis=-1      )
        a		:   Optional[int]	  =	jnp.max(logprobs_k[: self.timestamp_begin]      )
        return jnp.where(
            timestamp_logprob > max_text_token_logprob		,     scores_k.at[: self.timestamp_begin].set(-float('inf'      )      )		,     __snake_case		,     )

      a		:   str	  =	jax.vmap(__snake_case      )(__snake_case		,     __snake_case      )

      return scores




'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


lowerCAmelCase:   List[str]					=		{
    'configuration_falcon': ['FALCON_PRETRAINED_CONFIG_ARCHIVE_MAP', 'FalconConfig'],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCAmelCase:   str					=		[
         'FALCON_PRETRAINED_MODEL_ARCHIVE_LIST',
         'FalconForCausalLM',
         'FalconModel',
         'FalconPreTrainedModel',
         'FalconForSequenceClassification',
         'FalconForTokenClassification',
         'FalconForQuestionAnswering',
     ]


if TYPE_CHECKING:
     from .configuration_falcon import FALCON_PRETRAINED_CONFIG_ARCHIVE_MAP, FalconConfig

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_falcon import (
              FALCON_PRETRAINED_MODEL_ARCHIVE_LIST,
              FalconForCausalLM,
              FalconForQuestionAnswering,
              FalconForSequenceClassification,
              FalconForTokenClassification,
              FalconModel,
              FalconPreTrainedModel,
          )


else:
     import sys

     lowerCAmelCase:   Optional[int]					=		_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)